An automated non-destructive prediction of peroxide value and free fatty acid level in mixed nut samples

This study aimed to develop an automated technique, which is rapid, non-destructive and inexpensive, to test for rancidity of nuts. A visible to near infrared benchtop hyperspectral camera was used to capture images from blanched canarium, unblanched canarium and macadamia samples. Support vector machine classification (SVC) and PLSR models were developed to segregate the pooled spectra of the nuts and predict their peroxide values (PV) and free fatty acid (FFA) concentrations. The SVC and PLSR models were then used in a hierarchical model to develop an automated system for predicting PV and FFA. The automated model was then tested using a test set providing classification accuracy of 87% and R2 between 0.60 and 0.76 and RPD between 1.6 and 2.7 for PV and FFA prediction. Overall, the automated system has the potential commercial application in nut processing to detect rancidity of mixed nut samples non-destructively and in real-time. It is suggested to train other machine learning models with more samples to improve the accuracy of predictions

Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat

Hyperspectral imaging (HSI) is an emerging rapid and non-destructive technology that has promising application within feed mills and processing plants in poultry and other intensive animal industries. HSI may be advantageous over near infrared spectroscopy (NIRS) as it scans entire samples, which enables compositional gradients and sample heterogenicity to be visualised and analysed. This study was a preliminary investigation to compare the performance of HSI with that of NIRS for quality measurements of ground samples of Australian wheat and to identify the most important spectral regions for predicting carbon (C) and nitrogen (N) concentrations. In total, 69 samples were scanned using an NIRS (400–2500 nm), and two HSI cameras operated in 400–1000 nm (VNIR) and 1000–2500 nm (SWIR) spectral regions. Partial least square regression (PLSR) models were used to correlate C and N concentrations of 63 calibration samples with their spectral reflectance, with 6 additional samples used for testing the models. The accuracy of the HSI predictions (full spectra) were similar or slightly higher than those of NIRS (NIRS Rc2 for C = 0.90 and N = 0.96 vs. HSI Rc2 for C (VNIR) = 0.97 and N (SWIR) = 0.97). The most important spectral region for C prediction identified using HSI reflectance was 400–550 nm with R2 of 0.93 and RMSE of 0.17% in the calibration set and R2 of 0.86, RMSE of 0.21% and ratio of performance to deviation (RPD) of 2.03 in the test set. The most important spectral regions for predicting N concentrations in the feed samples included 1451–1600 nm, 1901–2050 nm and 2051–2200 nm, providing prediction with R2 ranging from 0.91 to 0.93, RMSE ranging from 0.06% to 0.07% in the calibration sets, R2 from 0.96 to 0.99, RMSE of 0.06% and RPD from 3.47 to 3.92 in the test sets. The prediction accuracy of HSI and NIRS were comparable possibly due to the larger statistical population (larger number of pixels) that HSI provided, despite the fact that HSI had smaller spectral range compared with that of NIRS. In addition, HSI enabled visualising the variability of C and N in the samples. Therefore, HSI is advantageous compared to NIRS as it is a multifunctional tool that poses many potential applications in data collection and quality assurance within feed mills and poultry processing plants. The ability to more accurately measure and visualise the properties of feed ingredients has potential economic benefits and therefore additional investigation and development of HSI in this application is warranted

Combination of Inorganic Nitrogen and Organic Soil Amendment Improves Nitrogen Use Efficiency While Reducing Nitrogen Runoff

Improved nitrogen fertiliser management and increased nitrogen use efficiency (NUE) can be achieved by synchronising nitrogen (N) availability with plant uptake requirements. Organic materials in conjunction with inorganic fertilisers provide a strategy for supplying plant-available N over the growing season and reducing N loss. This study investigated whether a combined application of inorganic N with an organic soil amendment could improve nitrogen use efficiency by reducing N loss in runoff. Nitrogen runoff from a ryegrass (Lolium multiflorum) cover was investigated using a rainfall simulator. Nitrogen was applied at low, medium and high (50, 75 and 100 kg/ha) rates as either (NH4)2SO4 or in combination with a poultry manure-based organic material. We showed that the NUE in the combination (58–75%) was two-fold greater than in (NH4)2SO4 (24–42%). Furthermore, this combination also resulted in a two-fold lower N runoff compared with the inorganic fertiliser alone. This effect was attributed to the slower rate of N release from the organic amendment relative to the inorganic fertiliser. Here, we demonstrated that the combined use of inorganic and organic N substrates can reduce nutrient losses in surface runoff due to a better synchronisation of N availability with plant uptake requirements. View Full-Tex

Combination of Inorganic Nitrogen and Organic Soil Amendment Improves Nitrogen Use Efficiency While Reducing Nitrogen Runoff

Improved nitrogen fertiliser management and increased nitrogen use efficiency (NUE) can be achieved by synchronising nitrogen (N) availability with plant uptake requirements. Organic materials in conjunction with inorganic fertilisers provide a strategy for supplying plant-available N over the growing season and reducing N loss. This study investigated whether a combined application of inorganic N with an organic soil amendment could improve nitrogen use efficiency by reducing N loss in runoff. Nitrogen runoff from a ryegrass (Lolium multiflorum) cover was investigated using a rainfall simulator. Nitrogen was applied at low, medium and high (50, 75 and 100 kg/ha) rates as either (NH4)2SO4 or in combination with a poultry manure-based organic material. We showed that the NUE in the combination (58–75%) was two-fold greater than in (NH4)2SO4 (24–42%). Furthermore, this combination also resulted in a two-fold lower N runoff compared with the inorganic fertiliser alone. This effect was attributed to the slower rate of N release from the organic amendment relative to the inorganic fertiliser. Here, we demonstrated that the combined use of inorganic and organic N substrates can reduce nutrient losses in surface runoff due to a better synchronisation of N availability with plant uptake requirements. View Full-Tex

A Performance Evaluation of Vis/NIR Hyperspectral Imaging to Predict Curcumin Concentration in Fresh Turmeric Rhizomes

Hyperspectral image (HSI) analysis has the potential to estimate organic compounds in plants and foods. Curcumin is an important compound used to treat a range of medical conditions. Therefore, a method to rapidly determine rhizomes with high curcumin content on-farm would be of significant advantage for farmers. Curcumin content of rhizomes varies within, and between varieties but current chemical analysis methods are expensive and time consuming. This study compared curcumin in three turmeric (Curcuma longa) varieties and examined the potential for laboratory-based HSI to rapidly predict curcumin using the visible–near infrared (400–1000 nm) spectrum. Hyperspectral images (n = 152) of the fresh rhizome outer-skin and flesh were captured, using three local varieties (yellow, orange, and red). Distribution of curcuminoids and total curcumin was analysed. Partial least squares regression (PLSR) models were developed to predict total curcumin concentrations. Total curcumin and the proportion of three curcuminoids differed significantly among all varieties. Red turmeric had the highest total curcumin concentration (0.83 ± 0.21%) compared with orange (0.37 ± 0.12%) and yellow (0.02 ± 0.02%). PLSR models predicted curcumin using raw spectra of rhizome flesh and pooled data for all three varieties (R2c = 0.83, R2p = 0.55, ratio of prediction to deviation (RPD) = 1.51) and was slightly improved by using images of a single variety (orange) only (R2c = 0.85, R2p = 0.62, RPD = 1.65). However, prediction of curcumin using outer-skin of rhizomes was poor (R2c = 0.64, R2p = 0.37, RPD = 1.28). These models can discriminate between ‘low’ and ‘high’ values and so may be adapted into a two-level grading system. HSI has the potential to help identify turmeric rhizomes with high curcumin concentrations and allow for more efficient refinement into curcumin for medicinal purposes

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes. View Full-Tex

Rapid Determination of Nutrient Concentrations in Hass Avocado Fruit by Vis/NIR Hyperspectral Imaging of Flesh or Skin

Fatty acid composition and mineral nutrient concentrations can affect the nutritional and postharvest properties of fruit and so assessing the chemistry of fresh produce is important for guaranteeing consistent quality throughout the value chain. Current laboratory methods for assessing fruit quality are time-consuming and often destructive. Non-destructive technologies are emerging that predict fruit quality and can minimise postharvest losses, but it may be difficult to develop such technologies for fruit with thick skin. This study aimed to develop laboratory-based hyperspectral imaging methods (400–1000 nm) for predicting proportions of six fatty acids, ratios of saturated and unsaturated fatty acids, and the concentrations of 14 mineral nutrients in Hass avocado fruit from 219 flesh and 194 skin images. Partial least squares regression (PLSR) models predicted the ratio of unsaturated to saturated fatty acids in avocado fruit from both flesh images (R2 = 0.79, ratio of prediction to deviation (RPD) = 2.06) and skin images (R2 = 0.62, RPD = 1.48). The best-fit models predicted parameters that affect postharvest processing such as the ratio of oleic:linoleic acid from flesh images (R2 = 0.67, RPD = 1.63) and the concentrations of boron (B) and calcium (Ca) from flesh images (B: R2 = 0.61, RPD = 1.51; Ca: R2 = 0.53, RPD = 1.71) and skin images (B: R2 = 0.60, RPD = 1.55; Ca: R2 = 0.68, RPD = 1.57). Many quality parameters predicted from flesh images could also be predicted from skin images. Hyperspectral imaging represents a promising tool to reduce postharvest losses of avocado fruit by determining internal fruit quality of individual fruit quickly from flesh or skin images

Underwater hyperspectral imaging technology has potential to differentiate and monitor scallop populations

Accurate and low-impact monitoring of scallop abundance is critical for stock assessment, especially in sensitive habitats. The possibility of using low-impact hyperspectral imaging (HSI) for differentiating scallop species in the marine environment was investigated. Live saucer (Ylistrum balloti) and mud (Ylistrum pleuronectes) scallops (N =  31) were scanned inside a sea simulator using a visible to near infrared (400–1000 nm) line-scanner HSI camera. Partial least square discriminant analysis (PLS-DA) was trained to distinguish between the species using their spectral signatures. Important wavelengths were identified and new models were developed using these wavelengths to reduce the model complexity and potentially increase the imaging speed when applied under at-sea conditions. The PLS-DA model distinguished between saucer and mud scallops using any area of the left valve that was exposed above the sediments, with 90.73% accuracy when all 462 available wavelengths were used. Using the subset of important wavelengths (N = 13) reduced the classification accuracy to 84%. Overall, our results showed that HSI has potential for detecting, distinguishing and counting commercially important saucer scallops for low-impact monitoring and resource management, and to complement RGB imaging that relies solely on morphological properties